Day 13 - Geospatial visualization
Objectives
library(tidyverse)
library(knitr)
library(broom)
library(stringr)
library(modelr)
library(forcats)
library(ggmap)
library(plotly)
options(digits = 3)
set.seed(1234)
theme_set(theme_minimal())Geospatial visualization
Geospatial visualizations are some of the oldest data visualization methods in human existence. Data maps were first popularized in the seventeenth century and have grown in complexity and detail since then. Consider Google Maps, the sheer volume of data depicted, and the analytical pathways available to its users.
Geometric visualizations are used to depict spatial features, and with the incorporation of data reveal additional attributes and information. The main features of a map are defined by its scale (the proportion between distances and sizes on the map), its projection (how the three-dimensional Earth is represented on a two-dimensional surface), and its symbols (how data is depicted and visualized on the map).1
Map boundaries
Drawing maps in R is a layer-like process. Typically you start by drawing the boundaries of the geographic regions you wish to visualize, then you add additional layers defined by other variables:
- Points
- Symbols
- Lines
- Fills (choropleths)
Let’s start by first drawing a map’s boundaries. Later on we will fill these in with data and turn them into data visualizations.
Storing map boundaries
A geographic information system (GIS) is software that is “designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data”.2 There are many specialized software packages for spatial data analysis, many of which are commercial or proprietary software. For serious spatial data analysis tasks, you probably want to learn how to use these products. However for casual usage, R has a number of tools for drawing maps, most importantly ggplot2.
Using maps boundaries
The maps package includes the map() function for drawing maps based on bundled geodatabases using graphics package:
library(maps)
# map of the world
map()
# usa boundaries
map("usa")
map("state")
# county map of illinois
map("county", "illinois")
These are fine, but we’d rather use them with our friendly ggplot2 library. We can do this by converting the geodatabases into data frames for plotting with ggplot2.
# map of the world
map_data("world") %>%
as_tibble## # A tibble: 99,338 × 6
## long lat group order region subregion
## * <dbl> <dbl> <dbl> <int> <chr> <chr>
## 1 -69.9 12.5 1 1 Aruba <NA>
## 2 -69.9 12.4 1 2 Aruba <NA>
## 3 -69.9 12.4 1 3 Aruba <NA>
## 4 -70.0 12.5 1 4 Aruba <NA>
## 5 -70.1 12.5 1 5 Aruba <NA>
## 6 -70.1 12.6 1 6 Aruba <NA>
## 7 -70.0 12.6 1 7 Aruba <NA>
## 8 -70.0 12.6 1 8 Aruba <NA>
## 9 -69.9 12.5 1 9 Aruba <NA>
## 10 -69.9 12.5 1 10 Aruba <NA>
## # ... with 99,328 more rows# usa boundaries
map_data("usa") %>%
as_tibble## # A tibble: 7,243 × 6
## long lat group order region subregion
## * <dbl> <dbl> <dbl> <int> <chr> <chr>
## 1 -101 29.7 1 1 main <NA>
## 2 -101 29.7 1 2 main <NA>
## 3 -101 29.7 1 3 main <NA>
## 4 -101 29.6 1 4 main <NA>
## 5 -101 29.6 1 5 main <NA>
## 6 -101 29.6 1 6 main <NA>
## 7 -101 29.6 1 7 main <NA>
## 8 -101 29.6 1 8 main <NA>
## 9 -101 29.6 1 9 main <NA>
## 10 -101 29.6 1 10 main <NA>
## # ... with 7,233 more rowsmap_data("state") %>%
as_tibble## # A tibble: 15,537 × 6
## long lat group order region subregion
## * <dbl> <dbl> <dbl> <int> <chr> <chr>
## 1 -87.5 30.4 1 1 alabama <NA>
## 2 -87.5 30.4 1 2 alabama <NA>
## 3 -87.5 30.4 1 3 alabama <NA>
## 4 -87.5 30.3 1 4 alabama <NA>
## 5 -87.6 30.3 1 5 alabama <NA>
## 6 -87.6 30.3 1 6 alabama <NA>
## 7 -87.6 30.3 1 7 alabama <NA>
## 8 -87.6 30.3 1 8 alabama <NA>
## 9 -87.7 30.3 1 9 alabama <NA>
## 10 -87.8 30.3 1 10 alabama <NA>
## # ... with 15,527 more rows# county map of illinois
map_data("county", "illinois") %>%
as_tibble## # A tibble: 1,697 × 6
## long lat group order region subregion
## * <dbl> <dbl> <dbl> <int> <chr> <chr>
## 1 -91.5 40.2 1 1 illinois adams
## 2 -90.9 40.2 1 2 illinois adams
## 3 -90.9 40.2 1 3 illinois adams
## 4 -90.9 40.1 1 4 illinois adams
## 5 -90.9 39.8 1 5 illinois adams
## 6 -90.9 39.8 1 6 illinois adams
## 7 -91.4 39.8 1 7 illinois adams
## 8 -91.4 39.8 1 8 illinois adams
## 9 -91.4 39.9 1 9 illinois adams
## 10 -91.5 39.9 1 10 illinois adams
## # ... with 1,687 more rowsmap_data() converts the geodatabases into data frames where each row is a single point on the map. The resulting data frame contains the following variables:
long- longitude. Things to the west of the prime meridian are negativelat- latitudeorder- this identifies the orderggplot()should follow to “connect the dots” and draw the bordersregionandsubregionidentify what region or subregion a set of points surroundsgroup- this is perhaps the most important variable in the data frame.ggplot()uses thegroupaesthetic to determine whether adjacent points should be connected by a line. If they are in the same group, then the points are connected. If not, then they are not connected. This is the same basic principle for standardgeom_line()plots:library(gapminder) # no group aesthetic ggplot(gapminder, aes(year, lifeExp)) + geom_line()
# with grouping by country ggplot(gapminder, aes(year, lifeExp, group = country)) + geom_line()
Drawing the United States
Let’s draw a map of the United States. First we need to store the USA map boundaries in a data frame:
usa <- map_data("usa") %>%
as_tibble
usa## # A tibble: 7,243 × 6
## long lat group order region subregion
## * <dbl> <dbl> <dbl> <int> <chr> <chr>
## 1 -101 29.7 1 1 main <NA>
## 2 -101 29.7 1 2 main <NA>
## 3 -101 29.7 1 3 main <NA>
## 4 -101 29.6 1 4 main <NA>
## 5 -101 29.6 1 5 main <NA>
## 6 -101 29.6 1 6 main <NA>
## 7 -101 29.6 1 7 main <NA>
## 8 -101 29.6 1 8 main <NA>
## 9 -101 29.6 1 9 main <NA>
## 10 -101 29.6 1 10 main <NA>
## # ... with 7,233 more rowsSimple black map
- We can use
geom_polygon()to draw lines between points and close them up (connect the last point with the first point) x = longandy = lat- We map the
groupaesthetic to thegroupcolumn
ggplot() +
geom_polygon(data = usa, aes(x=long, y = lat, group = group))
Ta-da! A few things we want to immediately start thinking about. First, because latitude and longitude have absolute relations to one another, we need to fix the aspect ratio so that we don’t accidentially compress the graph in one dimension. Fixing the aspect ratio also means that even if you change the outer dimensions of the graph (i.e. adjust the window size), then the aspect ratio of the graph itself remains unchanged. We can do this using the coord_fixed() function:
ggplot() +
geom_polygon(data = usa, aes(x=long, y = lat, group = group)) +
coord_fixed()
Now it looks a little squished. You can play around with the aspect ratio to find a better projection:3
ggplot() +
geom_polygon(data = usa, aes(x=long, y = lat, group = group)) +
coord_fixed(1.3)
Change the colors
Since this is a ggplot() object, we can change the fill and color aesthetics for the map:
ggplot() +
geom_polygon(data = usa, aes(x=long, y = lat, group = group),
fill = NA, color = "red") +
coord_fixed(1.3)
gg1 <- ggplot() +
geom_polygon(data = usa, aes(x=long, y = lat, group = group),
fill = "violet", color = "blue") +
coord_fixed(1.3)
gg1
Always remember to use the group aesthetic
What happens if we plot the map without the group aesthetic?
ggplot() +
geom_polygon(data = usa, aes(x=long, y = lat),
fill = "violet", color = "blue") +
coord_fixed(1.3)
Oops. The map doesn’t connect the dots in the correct order.
State maps
maps also comes with a state boundaries geodatabase:
states <- map_data("state") %>%
as_tibble()
states## # A tibble: 15,537 × 6
## long lat group order region subregion
## * <dbl> <dbl> <dbl> <int> <chr> <chr>
## 1 -87.5 30.4 1 1 alabama <NA>
## 2 -87.5 30.4 1 2 alabama <NA>
## 3 -87.5 30.4 1 3 alabama <NA>
## 4 -87.5 30.3 1 4 alabama <NA>
## 5 -87.6 30.3 1 5 alabama <NA>
## 6 -87.6 30.3 1 6 alabama <NA>
## 7 -87.6 30.3 1 7 alabama <NA>
## 8 -87.6 30.3 1 8 alabama <NA>
## 9 -87.7 30.3 1 9 alabama <NA>
## 10 -87.8 30.3 1 10 alabama <NA>
## # ... with 15,527 more rowsBy default, each state is filled with the same color:
ggplot(data = states) +
geom_polygon(aes(x = long, y = lat, group = group), color = "white") +
coord_fixed(1.3)
We can adjust this by using the fill aesthetic. Here, let’s map region to fill:
ggplot(data = states) +
geom_polygon(aes(x = long, y = lat, fill = region, group = group), color = "white") +
coord_fixed(1.3) +
# turn off color legend
theme(legend.position = "none")
Here, each state is assigned a different color at random. You can start to imagine how we might build a choropleth by mapping a different variable to fill, but we’ll return to that in a little bit.
Plot a subset of states
We can use filter() to subset the states data frame and draw a map with only a subset of the states. For example, if we want to only graph states in the Midwest:
midwest <- subset(states, region %in% c("illinois", "indiana", "iowa",
"kansas", "michigan", "minnesota",
"missouri", "nebraska", "north dakota",
"ohio", "south dakota", "wisconsin"))
ggplot(data = midwest) +
geom_polygon(aes(x = long, y = lat, group = group),
fill = "palegreen", color = "black") +
coord_fixed(1.3)
Zoom in on Illinois and its counties
First let’s get the Illinois boundaries:
(il_df <- filter(states, region == "illinois"))## # A tibble: 329 × 6
## long lat group order region subregion
## <dbl> <dbl> <dbl> <int> <chr> <chr>
## 1 -90.6 42.5 12 2951 illinois <NA>
## 2 -90.4 42.5 12 2952 illinois <NA>
## 3 -89.9 42.5 12 2953 illinois <NA>
## 4 -89.8 42.5 12 2954 illinois <NA>
## 5 -89.4 42.5 12 2955 illinois <NA>
## 6 -89.4 42.5 12 2956 illinois <NA>
## 7 -88.9 42.5 12 2957 illinois <NA>
## 8 -88.8 42.5 12 2958 illinois <NA>
## 9 -88.7 42.5 12 2959 illinois <NA>
## 10 -88.3 42.5 12 2960 illinois <NA>
## # ... with 319 more rowsNow let’s get the accompanying counties:
counties <- map_data("county")
(il_county <- filter(counties, region == "illinois"))## long lat group order region subregion
## 1 -91.5 40.2 561 21883 illinois adams
## 2 -90.9 40.2 561 21884 illinois adams
## 3 -90.9 40.1 561 21885 illinois adams
## 4 -90.9 39.8 561 21886 illinois adams
## 5 -90.9 39.8 561 21887 illinois adams
## 6 -91.4 39.8 561 21888 illinois adams
## 7 -91.4 39.8 561 21889 illinois adams
## 8 -91.4 39.9 561 21890 illinois adams
## 9 -91.5 39.9 561 21891 illinois adams
## 10 -91.4 39.9 561 21892 illinois adams
## 11 -91.4 39.9 561 21893 illinois adams
## 12 -91.5 40.0 561 21894 illinois adams
## 13 -91.5 40.0 561 21895 illinois adams
## 14 -91.5 40.2 561 21896 illinois adams
## 15 -91.5 40.2 561 21897 illinois adams
## 16 -89.2 37.3 562 21899 illinois alexander
## 17 -89.2 37.3 562 21900 illinois alexander
## 18 -89.2 37.3 562 21901 illinois alexander
## 19 -89.2 37.3 562 21902 illinois alexander
## 20 -89.2 37.3 562 21903 illinois alexander
## 21 -89.3 37.2 562 21904 illinois alexander
## 22 -89.3 37.2 562 21905 illinois alexander
## 23 -89.3 37.2 562 21906 illinois alexander
## 24 -89.3 37.2 562 21907 illinois alexander
## 25 -89.3 37.2 562 21908 illinois alexander
## 26 -89.3 37.2 562 21909 illinois alexander
## 27 -89.3 37.1 562 21910 illinois alexander
## 28 -89.3 37.1 562 21911 illinois alexander
## 29 -89.2 37.1 562 21912 illinois alexander
## 30 -89.2 37.1 562 21913 illinois alexander
## 31 -89.2 37.1 562 21914 illinois alexander
## 32 -89.2 37.1 562 21915 illinois alexander
## 33 -89.2 37.0 562 21916 illinois alexander
## 34 -89.2 37.0 562 21917 illinois alexander
## 35 -89.2 37.0 562 21918 illinois alexander
## 36 -89.2 37.1 562 21919 illinois alexander
## 37 -89.3 37.1 562 21920 illinois alexander
## 38 -89.3 37.1 562 21921 illinois alexander
## 39 -89.3 37.1 562 21922 illinois alexander
## 40 -89.3 37.0 562 21923 illinois alexander
## 41 -89.3 37.0 562 21924 illinois alexander
## 42 -89.3 37.0 562 21925 illinois alexander
## 43 -89.3 37.0 562 21926 illinois alexander
## 44 -89.4 37.0 562 21927 illinois alexander
## 45 -89.4 37.1 562 21928 illinois alexander
## 46 -89.4 37.1 562 21929 illinois alexander
## 47 -89.4 37.1 562 21930 illinois alexander
## 48 -89.4 37.2 562 21931 illinois alexander
## 49 -89.5 37.2 562 21932 illinois alexander
## 50 -89.5 37.3 562 21933 illinois alexander
## 51 -89.5 37.3 562 21934 illinois alexander
## 52 -89.5 37.3 562 21935 illinois alexander
## 53 -89.5 37.3 562 21936 illinois alexander
## 54 -89.5 37.3 562 21937 illinois alexander
## 55 -89.2 37.3 562 21938 illinois alexander
## 56 -89.3 38.7 563 21940 illinois bond
## 57 -89.6 38.7 563 21941 illinois bond
## 58 -89.6 38.9 563 21942 illinois bond
## 59 -89.6 38.9 563 21943 illinois bond
## 60 -89.6 39.0 563 21944 illinois bond
## 61 -89.6 39.0 563 21945 illinois bond
## 62 -89.6 39.0 563 21946 illinois bond
## 63 -89.3 39.0 563 21947 illinois bond
## 64 -89.3 39.0 563 21948 illinois bond
## 65 -89.3 39.0 563 21949 illinois bond
## 66 -89.3 38.7 563 21950 illinois bond
## 67 -88.9 42.2 564 21952 illinois boone
## 68 -88.9 42.5 564 21953 illinois boone
## 69 -88.8 42.5 564 21954 illinois boone
## 70 -88.7 42.5 564 21955 illinois boone
## 71 -88.7 42.2 564 21956 illinois boone
## 72 -88.9 42.2 564 21957 illinois boone
## 73 -90.9 39.8 565 21959 illinois brown
## 74 -90.9 40.1 565 21960 illinois brown
## 75 -90.7 40.1 565 21961 illinois brown
## 76 -90.7 40.1 565 21962 illinois brown
## 77 -90.7 40.0 565 21963 illinois brown
## 78 -90.7 40.0 565 21964 illinois brown
## 79 -90.6 40.0 565 21965 illinois brown
## 80 -90.6 40.0 565 21966 illinois brown
## 81 -90.6 40.0 565 21967 illinois brown
## 82 -90.5 40.0 565 21968 illinois brown
## 83 -90.5 40.0 565 21969 illinois brown
## 84 -90.5 39.9 565 21970 illinois brown
## 85 -90.6 39.9 565 21971 illinois brown
## 86 -90.6 39.9 565 21972 illinois brown
## 87 -90.6 39.8 565 21973 illinois brown
## 88 -90.9 39.8 565 21974 illinois brown
## 89 -89.6 41.6 566 21976 illinois bureau
## 90 -89.2 41.6 566 21977 illinois bureau
## 91 -89.2 41.3 566 21978 illinois bureau
## 92 -89.2 41.3 566 21979 illinois bureau
## 93 -89.3 41.3 566 21980 illinois bureau
## 94 -89.3 41.3 566 21981 illinois bureau
## 95 -89.3 41.3 566 21982 illinois bureau
## 96 -89.3 41.3 566 21983 illinois bureau
## 97 -89.4 41.2 566 21984 illinois bureau
## 98 -89.5 41.2 566 21985 illinois bureau
## 99 -89.5 41.1 566 21986 illinois bureau
## 100 -89.6 41.1 566 21987 illinois bureau
## 101 -89.6 41.2 566 21988 illinois bureau
## 102 -89.8 41.2 566 21989 illinois bureau
## 103 -89.8 41.5 566 21990 illinois bureau
## 104 -89.9 41.5 566 21991 illinois bureau
## 105 -89.9 41.6 566 21992 illinois bureau
## 106 -89.6 41.6 566 21993 illinois bureau
## 107 -90.9 39.4 567 21995 illinois calhoun
## 108 -90.6 39.4 567 21996 illinois calhoun
## 109 -90.6 39.4 567 21997 illinois calhoun
## 110 -90.6 39.3 567 21998 illinois calhoun
## 111 -90.6 39.3 567 21999 illinois calhoun
## 112 -90.6 39.2 567 22000 illinois calhoun
## 113 -90.6 39.2 567 22001 illinois calhoun
## 114 -90.6 39.2 567 22002 illinois calhoun
## 115 -90.6 39.1 567 22003 illinois calhoun
## 116 -90.6 39.1 567 22004 illinois calhoun
## 117 -90.6 39.0 567 22005 illinois calhoun
## 118 -90.6 39.0 567 22006 illinois calhoun
## 119 -90.6 39.0 567 22007 illinois calhoun
## 120 -90.5 38.9 567 22008 illinois calhoun
## 121 -90.5 39.0 567 22009 illinois calhoun
## 122 -90.5 39.0 567 22010 illinois calhoun
## 123 -90.5 38.9 567 22011 illinois calhoun
## 124 -90.5 38.9 567 22012 illinois calhoun
## 125 -90.6 38.9 567 22013 illinois calhoun
## 126 -90.6 38.9 567 22014 illinois calhoun
## 127 -90.6 38.9 567 22015 illinois calhoun
## 128 -90.7 38.9 567 22016 illinois calhoun
## 129 -90.7 38.9 567 22017 illinois calhoun
## 130 -90.7 39.0 567 22018 illinois calhoun
## 131 -90.7 39.1 567 22019 illinois calhoun
## 132 -90.7 39.1 567 22020 illinois calhoun
## 133 -90.7 39.1 567 22021 illinois calhoun
## 134 -90.7 39.2 567 22022 illinois calhoun
## 135 -90.7 39.2 567 22023 illinois calhoun
## 136 -90.7 39.2 567 22024 illinois calhoun
## 137 -90.8 39.3 567 22025 illinois calhoun
## 138 -90.8 39.3 567 22026 illinois calhoun
## 139 -90.9 39.4 567 22027 illinois calhoun
## 140 -90.9 39.4 567 22028 illinois calhoun
## 141 -89.9 42.2 568 22030 illinois carroll
## 142 -89.7 42.2 568 22031 illinois carroll
## 143 -89.7 41.9 568 22032 illinois carroll
## 144 -90.2 41.9 568 22033 illinois carroll
## 145 -90.1 42.0 568 22034 illinois carroll
## 146 -90.1 42.0 568 22035 illinois carroll
## 147 -90.1 42.0 568 22036 illinois carroll
## 148 -90.2 42.1 568 22037 illinois carroll
## 149 -90.2 42.1 568 22038 illinois carroll
## 150 -90.2 42.2 568 22039 illinois carroll
## 151 -90.3 42.2 568 22040 illinois carroll
## 152 -89.9 42.2 568 22041 illinois carroll
## 153 -90.5 40.0 569 22043 illinois cass
## 154 -90.5 40.0 569 22044 illinois cass
## 155 -90.4 40.0 569 22045 illinois cass
## 156 -90.4 40.0 569 22046 illinois cass
## 157 -90.4 40.1 569 22047 illinois cass
## 158 -90.4 40.1 569 22048 illinois cass
## 159 -90.4 40.1 569 22049 illinois cass
## 160 -90.4 40.1 569 22050 illinois cass
## 161 -90.3 40.1 569 22051 illinois cass
## 162 -90.3 40.1 569 22052 illinois cass
## 163 -90.3 40.1 569 22053 illinois cass
## 164 -90.3 40.0 569 22054 illinois cass
## 165 -90.3 40.1 569 22055 illinois cass
## 166 -90.2 40.0 569 22056 illinois cass
## 167 -90.2 40.1 569 22057 illinois cass
## 168 -90.2 40.1 569 22058 illinois cass
## 169 -90.1 40.1 569 22059 illinois cass
## 170 -90.1 40.1 569 22060 illinois cass
## 171 -90.1 40.1 569 22061 illinois cass
## 172 -90.0 40.1 569 22062 illinois cass
## 173 -90.0 40.1 569 22063 illinois cass
## 174 -90.0 40.1 569 22064 illinois cass
## 175 -90.0 39.9 569 22065 illinois cass
## 176 -90.0 39.9 569 22066 illinois cass
## 177 -90.6 39.9 569 22067 illinois cass
## 178 -90.6 39.9 569 22068 illinois cass
## 179 -90.5 39.9 569 22069 illinois cass
## 180 -90.5 40.0 569 22070 illinois cass
## 181 -90.5 40.0 569 22071 illinois cass
## 182 -88.5 40.3 570 22073 illinois champaign
## 183 -88.5 40.4 570 22074 illinois champaign
## 184 -88.0 40.4 570 22075 illinois champaign
## 185 -88.0 40.4 570 22076 illinois champaign
## 186 -87.9 40.4 570 22077 illinois champaign
## 187 -87.9 40.2 570 22078 illinois champaign
## 188 -87.9 40.2 570 22079 illinois champaign
## 189 -87.9 39.9 570 22080 illinois champaign
## 190 -88.5 39.9 570 22081 illinois champaign
## 191 -88.5 40.2 570 22082 illinois champaign
## 192 -88.5 40.2 570 22083 illinois champaign
## 193 -88.5 40.3 570 22084 illinois champaign
## 194 -89.0 39.7 571 22086 illinois christian
## 195 -89.0 39.4 571 22087 illinois christian
## 196 -89.1 39.4 571 22088 illinois christian
## 197 -89.5 39.4 571 22089 illinois christian
## 198 -89.5 39.5 571 22090 illinois christian
## 199 -89.5 39.6 571 22091 illinois christian
## 200 -89.5 39.6 571 22092 illinois christian
## 201 -89.5 39.7 571 22093 illinois christian
## 202 -89.4 39.7 571 22094 illinois christian
## 203 -89.4 39.8 571 22095 illinois christian
## 204 -89.4 39.7 571 22096 illinois christian
## 205 -89.4 39.8 571 22097 illinois christian
## 206 -89.3 39.8 571 22098 illinois christian
## 207 -89.3 39.8 571 22099 illinois christian
## 208 -89.3 39.8 571 22100 illinois christian
## 209 -89.3 39.8 571 22101 illinois christian
## 210 -89.2 39.8 571 22102 illinois christian
## 211 -89.2 39.8 571 22103 illinois christian
## 212 -89.2 39.8 571 22104 illinois christian
## 213 -89.1 39.8 571 22105 illinois christian
## 214 -89.1 39.8 571 22106 illinois christian
## 215 -89.1 39.7 571 22107 illinois christian
## 216 -89.0 39.7 571 22108 illinois christian
## 217 -88.0 39.2 572 22110 illinois clark
## 218 -88.0 39.4 572 22111 illinois clark
## 219 -88.0 39.5 572 22112 illinois clark
## 220 -88.0 39.5 572 22113 illinois clark
## 221 -87.7 39.5 572 22114 illinois clark
## 222 -87.7 39.5 572 22115 illinois clark
## 223 -87.5 39.5 572 22116 illinois clark
## 224 -87.5 39.4 572 22117 illinois clark
## 225 -87.6 39.3 572 22118 illinois clark
## 226 -87.6 39.3 572 22119 illinois clark
## 227 -87.6 39.3 572 22120 illinois clark
## 228 -87.6 39.3 572 22121 illinois clark
## 229 -87.6 39.2 572 22122 illinois clark
## 230 -87.6 39.2 572 22123 illinois clark
## 231 -87.6 39.2 572 22124 illinois clark
## 232 -87.6 39.2 572 22125 illinois clark
## 233 -87.8 39.2 572 22126 illinois clark
## 234 -87.8 39.2 572 22127 illinois clark
## 235 -88.0 39.2 572 22128 illinois clark
## 236 -88.0 39.2 572 22129 illinois clark
## 237 -88.3 38.6 573 22131 illinois clay
## 238 -88.7 38.6 573 22132 illinois clay
## 239 -88.7 38.8 573 22133 illinois clay
## 240 -88.7 38.9 573 22134 illinois clay
## 241 -88.4 38.9 573 22135 illinois clay
## 242 -88.4 38.9 573 22136 illinois clay
## 243 -88.3 38.9 573 22137 illinois clay
## 244 -88.3 38.7 573 22138 illinois clay
## 245 -88.3 38.7 573 22139 illinois clay
## 246 -88.3 38.7 573 22140 illinois clay
## 247 -88.3 38.7 573 22141 illinois clay
## 248 -88.3 38.7 573 22142 illinois clay
## 249 -88.3 38.6 573 22143 illinois clay
## 250 -88.3 38.6 573 22144 illinois clay
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## 1080 -90.6 39.9 629 23030 illinois morgan
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## 1507 -89.6 40.7 650 23478 illinois tazewell
## 1508 -89.6 40.7 650 23479 illinois tazewell
## 1509 -89.6 40.7 650 23480 illinois tazewell
## 1510 -89.6 40.8 650 23481 illinois tazewell
## 1511 -89.3 40.7 650 23482 illinois tazewell
## 1512 -89.3 40.6 650 23483 illinois tazewell
## 1513 -89.3 40.6 650 23484 illinois tazewell
## 1514 -89.3 40.6 650 23485 illinois tazewell
## 1515 -89.5 37.6 651 23487 illinois union
## 1516 -89.5 37.6 651 23488 illinois union
## 1517 -89.5 37.6 651 23489 illinois union
## 1518 -89.5 37.6 651 23490 illinois union
## 1519 -89.5 37.6 651 23491 illinois union
## 1520 -89.4 37.6 651 23492 illinois union
## 1521 -89.2 37.6 651 23493 illinois union
## 1522 -89.0 37.6 651 23494 illinois union
## 1523 -89.0 37.3 651 23495 illinois union
## 1524 -89.2 37.3 651 23496 illinois union
## 1525 -89.5 37.3 651 23497 illinois union
## 1526 -89.4 37.4 651 23498 illinois union
## 1527 -89.4 37.4 651 23499 illinois union
## 1528 -89.4 37.4 651 23500 illinois union
## 1529 -89.5 37.5 651 23501 illinois union
## 1530 -89.5 37.6 651 23502 illinois union
## 1531 -87.9 40.4 652 23504 illinois vermilion
## 1532 -87.9 40.5 652 23505 illinois vermilion
## 1533 -87.5 40.5 652 23506 illinois vermilion
## 1534 -87.5 40.5 652 23507 illinois vermilion
## 1535 -87.5 40.1 652 23508 illinois vermilion
## 1536 -87.5 39.9 652 23509 illinois vermilion
## 1537 -87.6 39.9 652 23510 illinois vermilion
## 1538 -87.6 39.9 652 23511 illinois vermilion
## 1539 -87.9 39.9 652 23512 illinois vermilion
## 1540 -87.9 40.2 652 23513 illinois vermilion
## 1541 -87.9 40.2 652 23514 illinois vermilion
## 1542 -87.9 40.4 652 23515 illinois vermilion
## 1543 -88.0 38.6 653 23517 illinois wabash
## 1544 -87.9 38.6 653 23518 illinois wabash
## 1545 -87.7 38.6 653 23519 illinois wabash
## 1546 -87.7 38.5 653 23520 illinois wabash
## 1547 -87.7 38.5 653 23521 illinois wabash
## 1548 -87.7 38.5 653 23522 illinois wabash
## 1549 -87.7 38.5 653 23523 illinois wabash
## 1550 -87.7 38.4 653 23524 illinois wabash
## 1551 -87.8 38.4 653 23525 illinois wabash
## 1552 -87.8 38.4 653 23526 illinois wabash
## 1553 -87.8 38.3 653 23527 illinois wabash
## 1554 -87.8 38.3 653 23528 illinois wabash
## 1555 -87.8 38.3 653 23529 illinois wabash
## 1556 -87.9 38.3 653 23530 illinois wabash
## 1557 -87.9 38.3 653 23531 illinois wabash
## 1558 -87.9 38.3 653 23532 illinois wabash
## 1559 -87.9 38.3 653 23533 illinois wabash
## 1560 -88.0 38.3 653 23534 illinois wabash
## 1561 -88.0 38.4 653 23535 illinois wabash
## 1562 -87.9 38.4 653 23536 illinois wabash
## 1563 -87.9 38.5 653 23537 illinois wabash
## 1564 -87.9 38.5 653 23538 illinois wabash
## 1565 -88.0 38.5 653 23539 illinois wabash
## 1566 -87.9 38.5 653 23540 illinois wabash
## 1567 -87.9 38.5 653 23541 illinois wabash
## 1568 -88.0 38.6 653 23542 illinois wabash
## 1569 -90.4 41.1 654 23544 illinois warren
## 1570 -90.4 40.7 654 23545 illinois warren
## 1571 -90.4 40.6 654 23546 illinois warren
## 1572 -90.8 40.6 654 23547 illinois warren
## 1573 -90.8 41.1 654 23548 illinois warren
## 1574 -90.4 41.1 654 23549 illinois warren
## 1575 -89.1 38.5 655 23551 illinois washington
## 1576 -89.1 38.2 655 23552 illinois washington
## 1577 -89.4 38.2 655 23553 illinois washington
## 1578 -89.5 38.2 655 23554 illinois washington
## 1579 -89.6 38.2 655 23555 illinois washington
## 1580 -89.7 38.2 655 23556 illinois washington
## 1581 -89.7 38.4 655 23557 illinois washington
## 1582 -89.7 38.4 655 23558 illinois washington
## 1583 -89.6 38.4 655 23559 illinois washington
## 1584 -89.6 38.5 655 23560 illinois washington
## 1585 -89.6 38.5 655 23561 illinois washington
## 1586 -89.6 38.5 655 23562 illinois washington
## 1587 -89.6 38.5 655 23563 illinois washington
## 1588 -89.5 38.5 655 23564 illinois washington
## 1589 -89.5 38.5 655 23565 illinois washington
## 1590 -89.5 38.5 655 23566 illinois washington
## 1591 -89.4 38.5 655 23567 illinois washington
## 1592 -89.4 38.5 655 23568 illinois washington
## 1593 -89.4 38.5 655 23569 illinois washington
## 1594 -89.3 38.5 655 23570 illinois washington
## 1595 -89.3 38.5 655 23571 illinois washington
## 1596 -89.2 38.5 655 23572 illinois washington
## 1597 -89.1 38.5 655 23573 illinois washington
## 1598 -89.1 38.5 655 23574 illinois washington
## 1599 -88.2 38.6 656 23576 illinois wayne
## 1600 -88.2 38.3 656 23577 illinois wayne
## 1601 -88.4 38.3 656 23578 illinois wayne
## 1602 -88.7 38.3 656 23579 illinois wayne
## 1603 -88.7 38.5 656 23580 illinois wayne
## 1604 -88.7 38.6 656 23581 illinois wayne
## 1605 -88.3 38.6 656 23582 illinois wayne
## 1606 -88.2 38.6 656 23583 illinois wayne
## 1607 -88.2 38.6 656 23584 illinois wayne
## 1608 -88.1 37.9 657 23586 illinois white
## 1609 -88.1 37.9 657 23587 illinois white
## 1610 -88.4 37.9 657 23588 illinois white
## 1611 -88.4 38.3 657 23589 illinois white
## 1612 -88.2 38.3 657 23590 illinois white
## 1613 -88.0 38.3 657 23591 illinois white
## 1614 -88.0 38.2 657 23592 illinois white
## 1615 -88.0 38.2 657 23593 illinois white
## 1616 -88.0 38.2 657 23594 illinois white
## 1617 -88.0 38.2 657 23595 illinois white
## 1618 -87.9 38.2 657 23596 illinois white
## 1619 -87.9 38.2 657 23597 illinois white
## 1620 -87.9 38.1 657 23598 illinois white
## 1621 -88.0 38.1 657 23599 illinois white
## 1622 -88.0 38.0 657 23600 illinois white
## 1623 -88.0 38.0 657 23601 illinois white
## 1624 -88.0 37.9 657 23602 illinois white
## 1625 -88.0 37.9 657 23603 illinois white
## 1626 -88.1 37.9 657 23604 illinois white
## 1627 -88.1 37.9 657 23605 illinois white
## 1628 -89.6 41.9 658 23607 illinois whiteside
## 1629 -89.6 41.6 658 23608 illinois whiteside
## 1630 -89.9 41.6 658 23609 illinois whiteside
## 1631 -90.2 41.6 658 23610 illinois whiteside
## 1632 -90.2 41.6 658 23611 illinois whiteside
## 1633 -90.2 41.7 658 23612 illinois whiteside
## 1634 -90.2 41.7 658 23613 illinois whiteside
## 1635 -90.2 41.7 658 23614 illinois whiteside
## 1636 -90.2 41.7 658 23615 illinois whiteside
## 1637 -90.2 41.7 658 23616 illinois whiteside
## 1638 -90.2 41.8 658 23617 illinois whiteside
## 1639 -90.2 41.8 658 23618 illinois whiteside
## 1640 -90.2 41.8 658 23619 illinois whiteside
## 1641 -90.2 41.9 658 23620 illinois whiteside
## 1642 -90.2 41.9 658 23621 illinois whiteside
## 1643 -89.7 41.9 658 23622 illinois whiteside
## 1644 -89.6 41.9 658 23623 illinois whiteside
## 1645 -89.6 41.9 658 23624 illinois whiteside
## 1646 -88.3 41.5 659 23626 illinois will
## 1647 -88.3 41.7 659 23627 illinois will
## 1648 -88.0 41.7 659 23628 illinois will
## 1649 -88.0 41.7 659 23629 illinois will
## 1650 -88.0 41.6 659 23630 illinois will
## 1651 -87.9 41.6 659 23631 illinois will
## 1652 -87.9 41.6 659 23632 illinois will
## 1653 -87.8 41.6 659 23633 illinois will
## 1654 -87.8 41.5 659 23634 illinois will
## 1655 -87.5 41.5 659 23635 illinois will
## 1656 -87.5 41.3 659 23636 illinois will
## 1657 -88.0 41.3 659 23637 illinois will
## 1658 -88.0 41.2 659 23638 illinois will
## 1659 -88.2 41.2 659 23639 illinois will
## 1660 -88.3 41.5 659 23640 illinois will
## 1661 -89.0 37.6 660 23642 illinois williamson
## 1662 -89.2 37.6 660 23643 illinois williamson
## 1663 -89.2 37.9 660 23644 illinois williamson
## 1664 -88.7 37.9 660 23645 illinois williamson
## 1665 -88.7 37.6 660 23646 illinois williamson
## 1666 -89.0 37.6 660 23647 illinois williamson
## 1667 -89.4 42.2 661 23649 illinois winnebago
## 1668 -89.4 42.5 661 23650 illinois winnebago
## 1669 -89.4 42.5 661 23651 illinois winnebago
## 1670 -88.9 42.5 661 23652 illinois winnebago
## 1671 -88.9 42.2 661 23653 illinois winnebago
## 1672 -89.2 42.2 661 23654 illinois winnebago
## 1673 -89.2 42.2 661 23655 illinois winnebago
## 1674 -89.4 42.2 661 23656 illinois winnebago
## 1675 -89.0 40.9 662 23658 illinois woodford
## 1676 -88.9 40.9 662 23659 illinois woodford
## 1677 -88.9 40.7 662 23660 illinois woodford
## 1678 -89.0 40.7 662 23661 illinois woodford
## 1679 -89.0 40.7 662 23662 illinois woodford
## 1680 -89.0 40.7 662 23663 illinois woodford
## 1681 -89.0 40.6 662 23664 illinois woodford
## 1682 -89.1 40.6 662 23665 illinois woodford
## 1683 -89.1 40.6 662 23666 illinois woodford
## 1684 -89.1 40.6 662 23667 illinois woodford
## 1685 -89.1 40.6 662 23668 illinois woodford
## 1686 -89.3 40.6 662 23669 illinois woodford
## 1687 -89.3 40.6 662 23670 illinois woodford
## 1688 -89.3 40.6 662 23671 illinois woodford
## 1689 -89.3 40.7 662 23672 illinois woodford
## 1690 -89.6 40.8 662 23673 illinois woodford
## 1691 -89.6 40.8 662 23674 illinois woodford
## 1692 -89.6 40.8 662 23675 illinois woodford
## 1693 -89.5 40.8 662 23676 illinois woodford
## 1694 -89.5 40.9 662 23677 illinois woodford
## 1695 -89.5 40.9 662 23678 illinois woodford
## 1696 -89.0 40.9 662 23679 illinois woodfordPlot the state first. This time, lets’ remove all the axes gridlines and background junk using theme_void():
il_base <- ggplot(data = il_df, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
geom_polygon(color = "black", fill = "gray")
il_base +
theme_nothing()
Now let’s plot the county boundaries in white:
il_base +
theme_void() +
geom_polygon(data = il_county, fill = NA, color = "white") +
geom_polygon(color = "black", fill = NA) # get the state border back on top
But what about Alaska and Hawaii?
If you were observant, you noticed map_data("states") only includes the 48 contiguous states in the United States. This is because Alaska and Hawaii exist far off from the rest of the states. What happens if you try to draw a map with them in it?
Yup, that doesn’t look right. Most maps of the United States place Alaska and Hawaii as insets to the south of California. Until recently, in R this was an extremely tedious task that required manually changing the latitude and longitude coordinates for these states to place them in the correct location. Fortunately a new package is available that has already done the work for you. fiftystater includes the fifty_states data frame which contains adjusted coordinates for Alaska and Hawaii to plot them with the mainland:
library(fiftystater)
data("fifty_states")
fifty_states %>%
as_tibble## # A tibble: 13,694 × 7
## long lat order hole piece id group
## <dbl> <dbl> <int> <lgl> <fctr> <chr> <fctr>
## 1 -85.1 32.0 1 FALSE 1 alabama Alabama.1
## 2 -85.1 31.9 2 FALSE 1 alabama Alabama.1
## 3 -85.1 31.9 3 FALSE 1 alabama Alabama.1
## 4 -85.1 31.8 4 FALSE 1 alabama Alabama.1
## 5 -85.1 31.8 5 FALSE 1 alabama Alabama.1
## 6 -85.1 31.7 6 FALSE 1 alabama Alabama.1
## 7 -85.1 31.7 7 FALSE 1 alabama Alabama.1
## 8 -85.1 31.7 8 FALSE 1 alabama Alabama.1
## 9 -85.1 31.6 9 FALSE 1 alabama Alabama.1
## 10 -85.0 31.6 10 FALSE 1 alabama Alabama.1
## # ... with 13,684 more rowsggplot(data = fifty_states, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
geom_polygon(color = "black", fill = "gray")
Using shapefiles
maps contains a very limited number of geodatabases. If you want to import a different country’s borders or some other geographic information, you will likely find the data in a shapefile. This is a special file format that encodes points, lines, and polygons in geographic space. Files appear with a .shp extension, sometimes with accompanying files ending in .dbf and .prj.
.shpstores the geographic coordinates of the geographic features (e.g. country, state, county).dbfstores data associated with the geographic features (e.g. unemployment rate, crime rates, percentage of votes cast for Donald Trump).prjstores information about the projection of the coordinates in the shapefile (we’ll handle this shortly)
Let’s start with a shapefile for state boundaries in the United States.4 We’ll use readShapePoly() from the maptools package to read in the data file:
library(rgdal)
usa <- readOGR("data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp")## OGR data source with driver: ESRI Shapefile
## Source: "data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp", layer: "cb_2013_us_state_20m"
## with 52 features
## It has 9 fields
## Integer64 fields read as strings: ALAND AWATERstr(usa, max.level = 2)## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 52 obs. of 9 variables:
## ..@ polygons :List of 52
## ..@ plotOrder : int [1:52] 32 48 29 3 18 46 33 22 34 51 ...
## ..@ bbox : num [1:2, 1:2] -179.1 17.9 179.8 71.4
## .. ..- attr(*, "dimnames")=List of 2
## ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slotThis is decidedly not a tidy data frame. Once you import the shapefile, it’s best to convert it to a data frame for ggplot(). We can do this using fortify():
usa %>%
fortify() %>%
head## long lat order hole piece id group
## 1 -88.5 31.9 1 FALSE 1 0 0.1
## 2 -88.5 31.9 2 FALSE 1 0 0.1
## 3 -88.5 31.9 3 FALSE 1 0 0.1
## 4 -88.5 31.9 4 FALSE 1 0 0.1
## 5 -88.5 32.0 5 FALSE 1 0 0.1
## 6 -88.5 32.0 6 FALSE 1 0 0.1Under this approach, the id variable is just a number assigned to each region (in this case, each state/territory). However the shapefile contains linked data with attributes for each region. We can access this using the @data accessor:
usa@data %>%
as_tibble## # A tibble: 52 × 9
## STATEFP STATENS AFFGEOID GEOID STUSPS NAME LSAD
## * <fctr> <fctr> <fctr> <fctr> <fctr> <fctr> <fctr>
## 1 01 01779775 0400000US01 01 AL Alabama 00
## 2 05 00068085 0400000US05 05 AR Arkansas 00
## 3 06 01779778 0400000US06 06 CA California 00
## 4 09 01779780 0400000US09 09 CT Connecticut 00
## 5 12 00294478 0400000US12 12 FL Florida 00
## 6 13 01705317 0400000US13 13 GA Georgia 00
## 7 16 01779783 0400000US16 16 ID Idaho 00
## 8 17 01779784 0400000US17 17 IL Illinois 00
## 9 18 00448508 0400000US18 18 IN Indiana 00
## 10 20 00481813 0400000US20 20 KS Kansas 00
## # ... with 42 more rows, and 2 more variables: ALAND <fctr>, AWATER <fctr>We can keep these variables in the new data frame through parameters to fortify(region = ""):
# state name
usa %>%
fortify(region = "NAME") %>%
head## long lat order hole piece id group
## 1 -88.5 31.9 1 FALSE 1 Alabama Alabama.1
## 2 -88.5 31.9 2 FALSE 1 Alabama Alabama.1
## 3 -88.5 31.9 3 FALSE 1 Alabama Alabama.1
## 4 -88.5 31.9 4 FALSE 1 Alabama Alabama.1
## 5 -88.5 32.0 5 FALSE 1 Alabama Alabama.1
## 6 -88.5 32.0 6 FALSE 1 Alabama Alabama.1# FIPS code
usa %>%
fortify(region = "STATEFP") %>%
head## long lat order hole piece id group
## 1 -88.5 31.9 1 FALSE 1 01 01.1
## 2 -88.5 31.9 2 FALSE 1 01 01.1
## 3 -88.5 31.9 3 FALSE 1 01 01.1
## 4 -88.5 31.9 4 FALSE 1 01 01.1
## 5 -88.5 32.0 5 FALSE 1 01 01.1
## 6 -88.5 32.0 6 FALSE 1 01 01.1# keep it all
(usa2 <- usa %>%
fortify(region = "NAME") %>%
as_tibble %>%
left_join(usa@data, by = c("id" = "NAME")))## # A tibble: 46,174 × 15
## long lat order hole piece id group STATEFP STATENS
## <dbl> <dbl> <int> <lgl> <fctr> <chr> <fctr> <fctr> <fctr>
## 1 -88.5 31.9 1 FALSE 1 Alabama Alabama.1 01 01779775
## 2 -88.5 31.9 2 FALSE 1 Alabama Alabama.1 01 01779775
## 3 -88.5 31.9 3 FALSE 1 Alabama Alabama.1 01 01779775
## 4 -88.5 31.9 4 FALSE 1 Alabama Alabama.1 01 01779775
## 5 -88.5 32.0 5 FALSE 1 Alabama Alabama.1 01 01779775
## 6 -88.5 32.0 6 FALSE 1 Alabama Alabama.1 01 01779775
## 7 -88.5 32.1 7 FALSE 1 Alabama Alabama.1 01 01779775
## 8 -88.4 32.2 8 FALSE 1 Alabama Alabama.1 01 01779775
## 9 -88.4 32.2 9 FALSE 1 Alabama Alabama.1 01 01779775
## 10 -88.4 32.2 10 FALSE 1 Alabama Alabama.1 01 01779775
## # ... with 46,164 more rows, and 6 more variables: AFFGEOID <fctr>,
## # GEOID <fctr>, STUSPS <fctr>, LSAD <fctr>, ALAND <fctr>, AWATER <fctr>Now we can plot it like normal using ggplot():
ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
geom_polygon(color = "black", fill = "gray")
Because the data file comes from the Census Bureau, we also get boundaries for Alaska, Hawaii, and Puerto Rico. To remove them from the data, just use filter():
usa2 <- usa2 %>%
filter(id != "Alaska", id != "Hawaii", id != "Puerto Rico")
ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
geom_polygon(color = "black", fill = "gray")
ggmap
Rather than relying on geodatabases or shapefiles which store boundaries as numeric data, we can use ggmap to retrieve raster map tiles from online mapping services.
library(ggmap)get_stamenmap(c(left = -87.6189, bottom = 41.7723, right = -87.5721, top = 41.8107), zoom = 14) %>%
ggmap() # NOTE: this will generate an error with ggplot2 v.2.2.1+
get_openstreetmap(bbox = c(left = -87.6189, bottom = 41.7723, right = -87.5721, top = 41.8107)) %>%
ggmap()## Error: map grabbing failed - see details in ?get_openstreetmap.get_googlemap("university of chicago", zoom = 12) %>%
ggmap()
Changing map projections
As you saw in The Truthful Art, representing portions of the globe on a flat surface can be challenging. Depending on how you project the map, you can distort or emphasize certain features of the map. Fortunately, ggplot() includes the coord_map() function which allows us to easily implement different projection methods.5
ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) +
geom_polygon(color = "black", fill = "gray") +
coord_map() +
ggtitle("Mercator projection (default)")
ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) +
geom_polygon(color = "black", fill = "gray") +
coord_map(projection = "albers", lat0 = 25, lat1 = 50) +
ggtitle("Albers equal-area projection")
ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) +
geom_polygon(color = "black", fill = "gray") +
coord_map(projection = "lambert", lat0 = 25, lat1 = 50) +
ggtitle("Lambert equal-area projection")
ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) +
geom_polygon(color = "black", fill = "gray") +
coord_map(projection = "conic", lat0 = 40) +
ggtitle("Conic projection")
ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) +
geom_polygon(color = "black", fill = "gray") +
coord_map(projection = "mollweide") +
ggtitle("Mollweide projection")
Adding data to the map
Region boundaries serve as the background in geospatial data visualization - so now we need to add data. Some types of channels (points, symbols, and lines) are overlaid on top of the boundaries, whereas other channels (fill) are incorporated into the region layer itself. Let’s look at the first set of channels.
Points
Let’s use our usa2 map data to add some points. The airports data frame in the nycflights13 package includes geographic info on airports in the United States.
library(nycflights13)
airports## # A tibble: 1,458 × 8
## faa name lat lon alt tz dst
## <chr> <chr> <dbl> <dbl> <int> <dbl> <chr>
## 1 04G Lansdowne Airport 41.1 -80.6 1044 -5 A
## 2 06A Moton Field Municipal Airport 32.5 -85.7 264 -6 A
## 3 06C Schaumburg Regional 42.0 -88.1 801 -6 A
## 4 06N Randall Airport 41.4 -74.4 523 -5 A
## 5 09J Jekyll Island Airport 31.1 -81.4 11 -5 A
## 6 0A9 Elizabethton Municipal Airport 36.4 -82.2 1593 -5 A
## 7 0G6 Williams County Airport 41.5 -84.5 730 -5 A
## 8 0G7 Finger Lakes Regional Airport 42.9 -76.8 492 -5 A
## 9 0P2 Shoestring Aviation Airfield 39.8 -76.6 1000 -5 U
## 10 0S9 Jefferson County Intl 48.1 -122.8 108 -8 A
## # ... with 1,448 more rows, and 1 more variables: tzone <chr>Each airport has it’s geographic location encoded through lat and lon. To draw these points on the map, basically we draw a scatterplot with x = lon and y = lat. In fact we could simply do that:
ggplot(airports, aes(lon, lat)) +
geom_point()
Let’s overlay it with the mapped state borders:
ggplot() +
coord_map() +
geom_polygon(data = usa2, mapping = aes(x = long, y = lat, group = group),
color = "black", fill = "gray") +
geom_point(data = airports, aes(x = lon, y = lat), shape = 1)
Slight problem. We have airports listed outside of the continental United States. Rather than trying to filter out airports, we can just crop the limits of the graph to only show the mainland:
ggplot() +
coord_map(xlim = c(-130, -60),
ylim = c(20, 50)) +
geom_polygon(data = usa2, mapping = aes(x = long, y = lat, group = group),
color = "black", fill = "gray") +
geom_point(data = airports, aes(x = lon, y = lat), shape = 1)
If we want to change the projection method, the points will automatically adjust too:
ggplot() +
coord_map(projection = "albers", lat0 = 25, lat1 = 50,
xlim = c(-130, -60),
ylim = c(20, 50)) +
geom_polygon(data = usa2, mapping = aes(x = long, y = lat, group = group),
color = "black", fill = "gray") +
geom_point(data = airports, aes(x = lon, y = lat), shape = 1)
Symbols
We can change the size or type of symbols on the map. For instance, we can draw a bubble plot (also known as a proportional symbol map) and encode the altitude of the airport through the size channel:
ggplot() +
coord_map(xlim = c(-130, -60),
ylim = c(20, 50)) +
geom_polygon(data = usa2, mapping = aes(x = long, y = lat, group = group),
color = "black", fill = "white") +
geom_point(data = airports, aes(x = lon, y = lat, size = alt),
fill = "grey", color = "black", alpha = .2) +
theme_void() +
theme(legend.position = "none")
Circle area is proportional to the airport’s altitude (in feet).
Lines
Drawing choropleth maps
Choropleth maps encode information by assigning shades of colors to defined areas on a map (e.g. countries, states, counties, zip codes). There are lots of ways to tweak and customize these graphs, which is generally a good idea because remember that color is one of the harder-to-decode channels.
Loading the data
We’ll continue to use the usa2 shapefile. Let’s also load and tidy the county shapefile:
usa <- readOGR("data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp")## OGR data source with driver: ESRI Shapefile
## Source: "data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp", layer: "cb_2013_us_state_20m"
## with 52 features
## It has 9 fields
## Integer64 fields read as strings: ALAND AWATERusa2 <- usa %>%
fortify(region = "GEOID") %>%
as_tibble %>%
left_join(usa@data, by = c("id" = "GEOID")) %>%
# filter out Alaska, Hawaii, Puerto Rico via FIPS codes
filter(!(STATEFP %in% c("02", "15", "72")))
counties <- readOGR("data/census_bureau/cb_2013_us_county_20m/cb_2013_us_county_20m.shp")## OGR data source with driver: ESRI Shapefile
## Source: "data/census_bureau/cb_2013_us_county_20m/cb_2013_us_county_20m.shp", layer: "cb_2013_us_county_20m"
## with 3221 features
## It has 9 fields
## Integer64 fields read as strings: ALAND AWATERcounties2 <- counties %>%
fortify(region = "GEOID") %>%
as_tibble %>%
left_join(counties@data, by = c("id" = "GEOID")) %>%
# filter out Alaska, Hawaii, Puerto Rico via FIPS codes
filter(!(STATEFP %in% c("02", "15", "72")))
ggplot(counties2, mapping = aes(x = long, y = lat, group = group)) +
geom_polygon(color = "black", fill = "gray") +
coord_map()
We’ll draw choropleths for the number of foreign-born individuals in each region (state or county). We can get those files from the census_bureau folder. Let’s also normalize our measure by the total population to get the rate of foreign-born individuals in the population:
(fb_state <- read_csv("data/census_bureau/ACS_13_5YR_B05012_state/ACS_13_5YR_B05012.csv") %>%
mutate(rate = HD01_VD03 / HD01_VD01))## # A tibble: 51 × 10
## GEO.id GEO.id2 `GEO.display-label` HD01_VD01 HD02_VD01 HD01_VD02
## <chr> <chr> <chr> <int> <chr> <int>
## 1 0400000US01 01 Alabama 4799277 <NA> 4631045
## 2 0400000US02 02 Alaska 720316 <NA> 669941
## 3 0400000US04 04 Arizona 6479703 <NA> 5609835
## 4 0400000US05 05 Arkansas 2933369 <NA> 2799972
## 5 0400000US06 06 California 37659181 <NA> 27483342
## 6 0400000US08 08 Colorado 5119329 <NA> 4623809
## 7 0400000US09 09 Connecticut 3583561 <NA> 3096374
## 8 0400000US10 10 Delaware 908446 <NA> 831683
## 9 0400000US11 11 District of Columbia 619371 <NA> 534142
## 10 0400000US12 12 Florida 19091156 <NA> 15392410
## # ... with 41 more rows, and 4 more variables: HD02_VD02 <int>,
## # HD01_VD03 <int>, HD02_VD03 <int>, rate <dbl>(fb_county <- read_csv("data/census_bureau/ACS_13_5YR_B05012_county/ACS_13_5YR_B05012.csv") %>%
mutate(rate = HD01_VD03 / HD01_VD01))## # A tibble: 3,143 × 10
## GEO.id GEO.id2 `GEO.display-label` HD01_VD01 HD02_VD01
## <chr> <chr> <chr> <int> <int>
## 1 0500000US01001 01001 Autauga County, Alabama 54907 NA
## 2 0500000US01003 01003 Baldwin County, Alabama 187114 NA
## 3 0500000US01005 01005 Barbour County, Alabama 27321 NA
## 4 0500000US01007 01007 Bibb County, Alabama 22754 NA
## 5 0500000US01009 01009 Blount County, Alabama 57623 NA
## 6 0500000US01011 01011 Bullock County, Alabama 10746 NA
## 7 0500000US01013 01013 Butler County, Alabama 20624 NA
## 8 0500000US01015 01015 Calhoun County, Alabama 117714 NA
## 9 0500000US01017 01017 Chambers County, Alabama 34145 NA
## 10 0500000US01019 01019 Cherokee County, Alabama 26034 NA
## # ... with 3,133 more rows, and 5 more variables: HD01_VD02 <int>,
## # HD02_VD02 <int>, HD01_VD03 <int>, HD02_VD03 <int>, rate <dbl>Joining the data to regions
Now that we have our data, we want to draw it on the map. To do that, we have to join together our data sources - the shapefiles and the CSVs. Normally joining data files requires a _join() operation of some sort. However when using ggplot2, we don’t have to do this. Remember that we can pass different data frames into different layers of a ggplot() object. Rather than using geom_polygon() to draw our maps, now we switch to geom_map():
ggplot(fb_state, aes(map_id = GEO.id2)) +
geom_map(aes(fill = rate), map = usa2) +
expand_limits(x = usa2$long, y = usa2$lat)
Let’s break down what just happened:
fb_stateis the data frame with the variables we want to visualizemap_id = GEO.id2identifies the column infb_statethat uniquely matches each observation to a region inusa2geom_map(aes(fill = rate), map = usa2)map = usa2is the data frame containing the boundary coordinatesfill = rateidentifies the column infb_statewe will use to determine the color of each region
expand_limits(x = usa2$long, y = usa2$lat)ensures the graph is drawn to the proper window. Because the default data frame for thisggplot()object isfb_state, it won’t contain the necessary information to size the window
We can then tweak this up by adding a title, removing the background (but retaining the legend), and projecting the map using a different method:
ggplot(fb_state, aes(map_id = GEO.id2)) +
geom_map(aes(fill = rate), map = usa2) +
expand_limits(x = usa2$long, y = usa2$lat) +
scale_fill_continuous(labels = scales::percent) +
labs(title = "Rate of foreign-born individuals in the population",
fill = NULL) +
ggthemes::theme_map() +
coord_map(projection = "albers", lat0 = 25, lat1 = 50)
We could do the same thing for the county-level data:
ggplot(fb_county, aes(map_id = GEO.id2)) +
geom_map(aes(fill = rate), map = counties2) +
expand_limits(x = counties2$long, y = counties2$lat) +
scale_fill_continuous(labels = scales::percent) +
labs(title = "Rate of foreign-born individuals in the population",
fill = NULL) +
ggthemes::theme_map() +
coord_map(projection = "albers", lat0 = 25, lat1 = 50)
Binning data
cut_interval()- makesngroups with equal range
fb_county %>%
mutate(rate_cut = cut_interval(rate, 6)) %>%
ggplot(aes(map_id = GEO.id2)) +
geom_map(aes(fill = rate_cut), map = counties2) +
expand_limits(x = counties2$long, y = counties2$lat) +
labs(title = "Rate of foreign-born individuals in the population",
fill = NULL) +
ggthemes::theme_map() +
coord_map(projection = "albers", lat0 = 25, lat1 = 50)
cut_number()- makesngroups with (approximately) equal numbers of observations
fb_county %>%
mutate(rate_cut = cut_number(rate, 6)) %>%
ggplot(aes(map_id = GEO.id2)) +
geom_map(aes(fill = rate_cut), map = counties2) +
expand_limits(x = counties2$long, y = counties2$lat) +
labs(title = "Rate of foreign-born individuals in the population",
fill = NULL) +
ggthemes::theme_map() +
coord_map(projection = "albers", lat0 = 25, lat1 = 50)
Defining colors
RColorBrewer
Sequential
fb_county %>%
mutate(rate_cut = cut_number(rate, 6)) %>%
ggplot(aes(map_id = GEO.id2)) +
geom_map(aes(fill = rate_cut), map = counties2) +
expand_limits(x = counties2$long, y = counties2$lat) +
labs(title = "Rate of foreign-born individuals in the population",
fill = NULL) +
ggthemes::theme_map() +
coord_map(projection = "albers", lat0 = 25, lat1 = 50) +
scale_fill_brewer(palette = "BuGn")
fb_county %>%
mutate(rate_cut = cut_number(rate, 6)) %>%
ggplot(aes(map_id = GEO.id2)) +
geom_map(aes(fill = rate_cut), map = counties2) +
expand_limits(x = counties2$long, y = counties2$lat) +
labs(title = "Rate of foreign-born individuals in the population",
fill = NULL) +
ggthemes::theme_map() +
coord_map(projection = "albers", lat0 = 25, lat1 = 50) +
scale_fill_brewer(palette = "YlGn")
fb_county %>%
mutate(rate_cut = cut_number(rate, 6)) %>%
ggplot(aes(map_id = GEO.id2)) +
geom_map(aes(fill = rate_cut), map = counties2) +
expand_limits(x = counties2$long, y = counties2$lat) +
labs(title = "Rate of foreign-born individuals in the population",
fill = NULL) +
ggthemes::theme_map() +
coord_map(projection = "albers", lat0 = 25, lat1 = 50) +
scale_fill_brewer(palette = "Blues")
Qualitative
state_data <- data_frame(name = state.name,
region = state.region,
subregion = state.division) %>%
bind_cols(as_tibble(state.x77)) %>%
# get id variable into data frame
left_join(usa2 %>%
select(id, NAME) %>%
distinct,
by = c("name" = "NAME")) %>%
# remove Alaska and Hawaii
na.omit
# set region base plot
region_p <- ggplot(state_data, aes(map_id = id)) +
geom_map(aes(fill = region), map = usa2) +
expand_limits(x = usa2$long, y = usa2$lat) +
labs(fill = NULL) +
ggthemes::theme_map() +
coord_map(projection = "albers", lat0 = 25, lat1 = 50)
region_p
# try different color brewers
region_p +
scale_fill_brewer(palette = "Paired")
region_p +
scale_fill_brewer(palette = "Dark2")
region_p +
scale_fill_brewer(palette = "Pastel2")
# set subregion base plot
subregion_p <- ggplot(state_data, aes(map_id = id)) +
geom_map(aes(fill = subregion), map = usa2) +
expand_limits(x = usa2$long, y = usa2$lat) +
labs(fill = NULL) +
ggthemes::theme_map() +
coord_map(projection = "albers", lat0 = 25, lat1 = 50)
subregion_p
subregion_p +
scale_fill_brewer(palette = "Paired")
subregion_p +
scale_fill_brewer(palette = "Set1")
subregion_p +
scale_fill_brewer(palette = "Pastel1")
Fixed size choropleths
Faceting maps
Making maps interactive
ggplotly()
leaflet
Acknowledgments
Session Info
devtools::session_info()## setting value
## version R version 3.3.3 (2017-03-06)
## system x86_64, darwin13.4.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/Chicago
## date 2017-05-05
##
## package * version date source
## assertthat 0.2.0 2017-04-11 cran (@0.2.0)
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